Assessment of Liver Diseases Using a Deep-Learning Approach Based on Ultrasound RF-Data

Not Applicable

Recruiting

• Conditions: Artificial Intelligence; Ultrasonography; Elasticity Imaging Techniques; Liver Diseases; Metastasis to Liver

• Registration Number: NCT06317181
• Lead Sponsor: Technische Universität Dresden

Brief Summary

The goal of this clinical trial is to test the performance of neuronal networks trained on ultrasonic raw Data (=radiofrequency data) for the assessment of liver diseases in patients undergoing a clinical ultrasound examination. The general feasibility is currently evaluated in a retrospective cohort.

The main questions the study aims to answer are:

* Can a neuronal network trained on RF Data perform equally good as elastography in the assessment of diffuse liver diseases?

* Can a neuronal network trained on RF Data perform better than a neuronal network trained on b-mode images in the assessment of diffuse liver diseases?

* Can a neuronal network trained on RF Data distinguish focal pathologies in the liver from healthy tissue?

To answer these questions participants with a clinically indicated fibroscan will undergo:

* a clinical elastography in Case ob suspected diffuse liver disease

* a reliable ground truth (if normal ultrasound is not sufficient e.g. contrast enhanced ultrasound, biopsy, MRI or CT) in case of focal liver diseases, depending on the standard routine of the participating center

* a clinical ultrasound examination during which b-mode images and the corresponding RF-Data sets are captured

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2024-03-01
• Study First Submitted QC: 2024-03-15
• Study First Posted: 2024-03-19
• Study Start: 2024-04-01
• Status Verified: 2024-11
• Last Update Submitted: 2024-11-24
• Last Update Posted: 2024-11-26
• Primary Completion: 2025-06
• Study Completion: 2025-12

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 200

Inclusion Criteria

• scheduled for an ultrasound investigation by an independent physician
• signed declaration of consent

Exclusion Criteria

• smaller interventions in the same liver during the last 2 Week (for example liver biopsy)
• contrast enhanced ultrasound less than a day ago
• major intervention at the liver (for example partial resection)

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
Performance analysis of the trained model	After study completion, estimated 1 year	Analysis of the concordance of a Deep Learning-based analysis of RF data with established clinical measures. In case of diffuse disease the stiffness of the tissue and in case of the focal lesions the underlying disease as diagnosed by the local physicians are the measures.; Performance is evaluated by the area under the receiver operating characteristic curve and a correlation coefficient.

Trial Locations

Locations (4)

Diakonissen Hospital Dresden; 🇩🇪 Dresden, Germany

University Hospital Halle (Saale); 🇩🇪 Halle (Saale), Germany

University Hospital Leipzig; 🇩🇪 Leipzig, Germany

University Hospital; 🇩🇪 Dresden, Germany